Control

Bringing people, systems, and workflows together

  • Connect care teams, systems, and workflows across technologies, sites, and treatment approaches
  • Support treatment continuity with the optional Backup Planner, enabling efficient plan conversion across machines, techniques, and energies
  • Maintain flexibility across current and next-generation treatment delivery systems, including Accela
  • Benefit from GPU-enabled optimization algorithms that help accelerate treatment plan generation
  • Tailor planning workflows with scripting and automation tools to support institutional needs

AI Contouring — Integrated in Eclipse

High-quality, deep learning-driven structure delineation directly within your treatment planning workflow

  • Automatically generates contours from CT and MRI images for over 200 anatomical structures — including organs-at-risk, lymph nodes, and brain metastases
  • Built on multiple clinical guidelines (RTOG, ESTRO, DAHANCA, and more) for consistent, evidence-based results
  • Seamlessly integrated within Eclipse, with no additional hardware, no separate platform, and single vendor support
  • Validated by leading institutions2,3 worldwide, with auto contouring results on par with expert physicians4

輔助 Eclipse

治療投照

Explore the entire Digital Oncology portfolio

我們可協助您建立聯繫

我們的專員可協助您將瓦里安系統整合至您的工作流程中。

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†獲 CE 標誌 (歐洲合格認證)。 510(k) 待處理。 本產品並未在美國或所有市場提供，且不保證商品化或功能可用性。

*AI-Rad Companion Organ RT 是西門子醫療設備公司的產品，也是西門子資料設備公司在部分國家/地區的註冊商標。

  1. Putz, F. Is AI-powered tumor autocontouring ready for clinical use? Evaluation of Siemens Healthineers research prototype for the autosegmentation of brain metastases. Poster presented at DEGRO; Jun 2025, Dresden, Germany. Datasets: 101 patients; 391 lesions; 1.5T MRI series T1-SPACE. University of Erlangen-Nuremberg.
  2. Tezcanli, E. Evaluation of a Deep Learning Contouring Algorithm for Target Delineation in HyperArc Radiosurgery. Webinar, Oct 2025. Datasets: 10 patients; 82 lesions. Acibadem University.